F1 Score

F1 score combines precision and recall into a single classification metric using their harmonic mean. The score is high only when both measures are high, making it useful for imbalanced datasets where ordinary accuracy can be deceptive. F1 does not include true negatives and assumes precision and recall deserve equal weight; variants such as F-beta adjust that balance. For multiclass tasks, macro, micro, and weighted averaging can produce very different results, so the averaging method must be stated. A single F1 value also hides threshold behavior and subgroup differences. Teams should examine the underlying confusion matrix and select an operating threshold based on real error costs.